Refinement of rice blast disease resistance QTLs and gene networks through meta-QTL analysis

Rice blast disease is the most devastating disease constraining crop productivity. Vertical resistance to blast disease is widely studied despite its instability. Clusters of genes or QTLs conferring blast resistance that offer durable horizontal resistance are important in resistance breeding. In this study, we aimed to refine the reported QTLs and identify stable meta-QTLs (MQTLs) associated with rice blast resistance. A total of 435 QTLs were used to project 71 MQTLs across all the rice chromosomes. As many as 199 putative rice blast resistance genes were identified within 53 MQTL regions. The genes included 48 characterized resistance gene analogs and related proteins, such as NBS–LRR type, LRR receptor-like kinase, NB-ARC domain, pathogenesis-related TF/ERF domain, elicitor-induced defense and proteins involved in defense signaling. MQTL regions with clusters of RGA were also identified. Fifteen highly significant MQTLs included 29 candidate genes and genes characterized for blast resistance, such as Piz, Nbs-Pi9, pi55-1, pi55-2, Pi3/Pi5-1, Pi3/Pi5-2, Pikh, Pi54, Pik/Pikm/Pikp, Pb1 and Pb2. Furthermore, the candidate genes (42) were associated with differential expression (in silico) in compatible and incompatible reactions upon disease infection. Moreover, nearly half of the genes within the MQTL regions were orthologous to those in O. sativa indica, Z. mays and A. thaliana, which confirmed their significance. The peak markers within three significant MQTLs differentiated blast-resistant and susceptible lines and serve as potential surrogates for the selection of blast-resistant lines. These MQTLs are potential candidates for durable and broad-spectrum rice blast resistance and could be utilized in blast resistance breeding.

eco-friendly approach.However, wide application, effectiveness, and durability can be achieved only through the identification of stable resistant sources and their utilization in crop breeding.Moreover, molecular markers have been used effectively to reduce screening and increase the efficiency of transfer of resistance in terms of pace and precision.Over a hundred blast resistance genes have been reported across all the rice chromosomes 1,7 .Most of these treatments are race specific and have limited durability.However, blast resistance seldom shows quantitative inheritance encompassing a cluster of genes 1 .Riveting on race-nonspecific and broad-spectrum genes helps achieve durable horizontal resistance 8 .In this context, the discovery and characterization of quantitative trait loci (QTLs) governing rice blast resistance are of paramount interest.
Several QTLs linked to blast resistance in rice have been reported from different environments using different mapping populations [9][10][11][12] .In addition, most of them (except for a few QTLs with major effects) are restrained from deployment in marker assisted selection (MAS), speculating uncertainty.The uncertainty or instability of QTLs is associated with different genetic backgrounds and growing environments 13 .QTL effects are influenced by epistasis and modifier effects, both of which are more sensitive to genetic background 14 .Furthermore, biased QTL-environment correlations will restrict the use of a QTL across different environments.Limited recombination in biparental mapping populations causes several problems, such as low population size, low logarithm of odds (LOD) score, genotyping errors, and phenotyping in minimal environments, which reduce the effectiveness of QTLs in MAS 15 .Despite these limitations, QTL mapping is a modest way to understand the genetics underlying the trait of interest 16 .Hence, identifying a robust consensus genomic region using QTL information from independent studies has found relevance.With the availability of vast rice genomic resources and databases, cataloging and compiling QTL information on a consensus map is feasible.Meta-QTL analysis is instrumental in integrating QTL information from independent studies to identify and project robust QTLs on a consensus map [16][17][18] .This approach aims to discover whether the QTL for a trait or related traits from different studies colocalize on the consensus map.Furthermore, this method identifies 'real QTL' with refined QTL positions and narrows its confidence interval (CI) 19 .A narrowed CI of a QTL decreases the number of genes predicted within the interval by more than that of the original QTL, thereby increasing the recovery of probable candidates for the trait 17 .
With this perspective, we framed the study by hypothesizing the presence of stable large-effect QTLs associated with rice blast resistance in the rice genome.Meta-QTL analysis was implemented to test this hypothesis with the following objectives: (a) projection of MQTLs conferring rice blast resistance considering the original QTL; (b) identification of peak (nearest) markers linked to MQTLs; and (c) mining of the candidate genes underlying the MQTL region and predicting their role in the rice blast resistance.

Discovery of MQTLs for rice blast resistance
Information on a total of 737 QTLs associated with rice blast resistance was collected from 53 independent studies reported over the past two decades (Table 1).These studies included recombinant inbred line (RIL, 40%), backcross (BC, 28%), F 2 (23%), and doubled haploid (DH, 9%) mapping populations (Fig. 1d) with sizes ranging from 31 to 1125 (Fig. 1a), which were evaluated for rice blast resistance across 14 different countries (Fig. 1e).Blast-resistant QTLs were reported across all the rice chromosomes, with the maximum occurring on chromosome 11 with 71 initial QTLs, and a minimum of 18 initial QTLs reported on chromosome 10 (Fig. 2a).The majority (682) of the initial QTLs were significant with a LOD > 2.5, except for a few (55) (Fig. 1b).The initial QTLs included 345 major-effect QTLs with a PVE ≥ 10%, while the remaining were minor-effect QTLs (Fig. 1c).A consensus map was generated using the individual maps from each of the 53 experiments.
In our study, all the chromosomes were projected with > 10 initial QTLs; hence, we followed Veyriera's approach for MQTL identification.A model with the lowest Akaike information criterion (AIC), corrected AIC (AICc), AIC3, Bayesian information criterion (BIC) and average weight of evidence (AWE) criteria was chosen for MQTL projection.Among the 737 initial QTL, only 435 QTLs were used in the meta-QTL projection due to low LOD scores and/or low PVE% and a large CI 13 (Fig. S1).As many as 71 MQTLs were distributed across all chromosomes, with each MQTL consisting of one to 19 initial QTLs projected (Fig. 2).Interestingly, the confidence interval of the MQTL region decreased to an extent of 0.04 Mb, while as many as 60 MQTLs showed a confidence interval ≤ 2 Mb.The significance of each MQTL was depicted by its weight, which ranged from 0.03 to 0.62.A total of 31 MQTLs were associated with weight > 0.15 (Table S1).Among these, the top fifteen MQTLs with a confidence interval ≤ 2 Mb and a weight > 0.15 were considered highly significant (Table 2).

Ontology of genes within the MQTL region
Gene ontologies of all the genes within MQTL regions were retrieved using the Ensembl database.Approximately 40% of the genes were associated with molecular functions, 29% were associated with cellular components, and 31% were associated with biological processes.The GO annotations of some of the genes involved in the biological processes included "autophagy", "calcium ion transmembrane transport", "cell cycle", "cell division", "cellular response to stimulus", "defense response", and "defense response to fungus", which may have a role in rice blast disease resistance" (Fig. 4a).Furthermore, 12% of the genes were assigned to the "tolerance and resistance' trait class, which included 32% of the disease resistance-conferring genes (Fig. 4b).The functional annotation of 199 candidate genes related to rice blast resistance revealed their significant involvement in 30 different functions (Table S5) with their fold enrichment, and an FDR < 0.05.These genes were predominantly associated with gene networks related to the response to stimulus, the response to stress and the defense response, protein modification, signal transduction, and the diterpenoid and cinnamic acid biosynthetic/ metabolic pathways.The number of genes shared among the functional annotations was represented by the network and clustering of pathways (Fig. S3).The genome-wide fold enrichment of the candidate genes indicated three enriched regions, two on chromosome 11 (MQTL11.8and 11.9) and one on chromosome 6 (MQTL6.3),which were associated with genes characterized for blast resistance (Fig. S4).Furthermore, we considered only the 29 rice blast candidate genes within the 15 most significant MQTLs for functional annotation.Genes within eight of these MQTLs were associated with 11 different pathways (Fig. 5).The genes within MQTL6.3 and MQTL9.2 were associated with the nucleotide-binding adaptor (NB-ARC) shared by APAF-1, R proteins, and CED-4 domain, while those within MQTL8.5 were involved in the heavy metal-associated domain superfamily.Furthermore, the Os09g0327600/Os09g0327800/Pi3 genes in MQTL9.2 and the Os10g0542800/OsBSK1-2 genes in MQTL10.3 were affiliated with the plant defense pathway (Table 4).

Validation of MQTLs
Five blast-resistant rice genotypes (mean score ≤ 4.5) and five susceptible genotypes (mean score ≥ 7.5) were genotyped using peak/nearest markers associated with each of the significant MQTLs (Table S1).Three of the peak markers, viz., RM17377, 40N23r and Pikh, in MQTL4.5, MQTL9.2 and MQTL11.8,respectively, differentiated resistant and susceptible groups (Figs. 6, S5).Among them, 40N23r and Pikh were reported to be gene based markers for the Pi5 and Pikh/Pi54 genes, respectively 23 .Thus, validation of these peak markers confirmed the significance of the corresponding MQTLs in blast disease resistance.
In silico validation of the candidate genes associated with rice blast resistance was performed by screening for microarray-based expression studies.The expression profiles of 150 blast resistance candidates identified within different MQTLs in our study were retrieved from the RXP_3001 dataset (gene expression profile in whole leaves inoculated with M. oryzae) available in the RiceXPRO database (https:// ricex pro.dna.affrc.go.jp/) 24 .The expression patterns of candidate genes at 1, 2, 3, and 5 days post inoculation (dpi) with two M. oryzae strains (P91-15B and Kyu77-07A) in the differential Nipponbare entries harboring the blast resistance genes Pia and Pish were examined.Among the 199 candidate genes, the expression profiles of 73 were not available in the database.Within the expression profiles of 126 genes, 51 were upregulated and 33 were downregulated at 1, 2, 3, and 5 dpi with both compatible and incompatible reactions (Fig. S6).The gene Os02g0584700 in MQTL2.3 showed differential expression between compatible and incompatible reactions with both strains, while the gene   www.nature.com/scientificreports/Os06g0163000/OsPUB70 in MQTL6.2 discerned differential expression with the Kyu77-07A strain (Table S3; Fig. S6).However, 42 genes, including eleven characterized genes, exhibited differential expression at different dpi.
The genes Os09g0327600 and Os06g0286700, which were infected with P91-15B and Kyu77-07A, respectively, were downregulated at 5 dpi in incompatible reactions.The expression of Os08g0511700 and Os09g0327600 in both of these strains was upregulated at 1 dpi and downregulated at 5 dpi, respectively.Furthermore, the expression of the Os09g0327800 gene was downregulated in the resistant combinations but upregulated in the susceptible combinations at 1 dpi with Kyu77-07A and at 2 dpi with the P91-15B strain (Fig. 7).A similar trend was observed for Os11g0225300/Pia-2, whose expression was downregulated in the resistant combinations but upregulated in the susceptible reactions at 1 dpi and 5 dpi with Kyu77-07A; the reverse was true for the P91-15B strain.Os11g0225100/Pia-1 was upregulated in the incompatible reaction but downregulated in the compatible reaction at 1 dpi and 2 dpi with the P91-15B strain.The gene Os11g0598500/Pb1 was downregulated in all reactions except for the resistance reaction with the P91-15B strain at 5 dpi.The genes Os11g0639100/Pi54/ Pikh, Os11g0682600/Pb2 and Os11g0689100/Pik/Pikm/Pikp were downregulated in the resistant reaction but upregulated in the susceptible reaction in all four treatments with the Kyu77-07A strain.However, the expression of these genes was unaltered in response to treatment with the P91-15B strain (Fig. 7).

Discussion
Sustained rice production to meet global food security needs has gained paramount importance with the increasing population.Rice productivity is significantly constrained by pests and diseases.These findings have led to the convergence of rice researchers' focus on resistance breeding against these constraints.Among several diseases, blast disease is one of the most devastating diseases in rice, causing yield losses of up to 100%.More than 100 genes and 350 QTLs conferring blast resistance have been identified, 37 of which have been cloned and characterized 1,9,10 .Most of these genes are race specific and impart highly fragile vertical resistance against blast disease.Diverse rice germplasms, including cultivars, landraces and wild species, are being screened for genes contributing to broad-spectrum resistance 25 .Disease resistance conferred by one or two major genes is effective but prone to breakdown with the emergence of new strains.Hence, gene clusters that provide broad-spectrum  resistance and thereby impart horizontal durable resistance should be focused on.QTLs linked to blast resistance are likely to harness horizontal resistance, which is durable 15 .However, the effectiveness of QTLs in MAS is limited by several factors, such as linkage drag due to the large QTL interval, the QTL projection model, and limited recombination events in the biparental mapping population 15 .Moreover, the instability of QTLs identified in a particular mapping population prevents their deployment in MAS across genetic backgrounds.Fine-mapping the QTLs and delimiting them to one or two genes, such as Pb-bd1 or Pi-69(t), followed by their characterization and utilization in breeding programme is effective 25,26 .However, fine mapping each of the many QTLs is unrealistic because it is time-and resource-demanding 9 .In this regard, the meta-QTL analysis approach could be useful for refining QTL intervals and subsequently validating their association with trait of interest 18 .
In the present study, initial QTLs were available on all the chromosomes, depicting the quantitative genetic architecture of blast disease resistance.A total of 71 MQTLs were projected from 435 initial QTLs reported in 53 independent studies.A reduction in the number of MQTLs from the initial QTLs indicated the colocation of the QTL and shrinkage of their confidence interval.A similar pattern was reported for rice blast 27 and rice grain weight 15 .Furthermore, approximately 3390 genes within the MQTL region exhibited high orthologity with other subspecies of rice (O.sativa indica group), cereal species (Z.mays) and A. thaliana.The orthology of these genes with related species verified the significance of the respective MQTLs and reiterated their stability across species 28 .The genes underlying the identified MQTLs were affiliated with ontologies such as response to stimulus, defense response, and response to fungus, which indicated their possible role in resistance against rice blast.Resistance to blast disease is conferred by resistance (R) genes, such as nucleotide binding site-leucine rich repeats (NBS-LRR or NLR), nucleotide binding adaptors shared by APAF-1, R proteins, and CED-4 (NB-ARC ), disease resistance proteins (DRPs), leucine rich repeats (LRRs) and pathogenesis related proteins (PRs) 20 .The identified MQTL regions consisted of 280 disease resistance genes.Among them, 199 genes (R genes and their analogs) within 29 MQTL regions were candidates for blast resistance involved in PAMP-triggered immunity, MAPK signaling and phytohormone signaling.As many as thirteen genes characterized for blast resistance, such as Piz, Nbs1-Pi9, pi55-1, pi55-2, Pi3, Pia/RGA4, Pia/RGA5, Pb1, Pi54, Pikh, Pb2 and Pik/Pikp/Pikm [29][30][31][32][33][34][35][36] , resided within the identified MQTL regions.These MQTLs harboring one or more characterized genes could potentially contribute to blast resistance across different genetic backgrounds.
According to the gene ontology, blast resistance candidates within 29 MQTL regions were involved in different defense pathways.For instance, MQTL2.3 consists of the Os02g0571100/OsCPS2 and Os02g0570400/OsKS7 genes, which are affiliated with diterpenoid biosynthetic/metabolic pathways that are known to influence blast resistance in rice 37 .Similarly, genes related to the defense response pathway were found within MQTL1.5, 2.3, 4.1, 9.2, 10.3, 11.1, 11.9 and 12.6, which signifies their role in plant defense.Furthermore, thirteen of the MQTLs on chromosomes 1, 2, 4, 5, 8, 10, 11 and 12 consisted of clusters of > 5 RGAs, and MQTL6.3 was enriched with a defense-related gene cluster on chromosome 6.These MQTL regions harboring gene clusters for blast resistance indicate their possible role in broad-spectrum resistance 10,21,22 .Furthermore, gene clusters or pairs of NLRs found on chromosome 11 (MQTL11.1,11.5, 11.8 and 11.9) have been proven to play a role in broad-spectrum resistance 20 .Introgression of MQTLs with RGA clusters works akin to pyramiding genes in conferring broadspectrum resistance; thus, introgression is advantageous over that of a single gene 20,21,38 .
The identified highly significant (weight > 15) and high-resolution MQTLs encompassed 887 genes, including 29 rice blast-associated genes.Among the significant MQTLs, MQTL3.1 harbors the gene Os03g0324600, which encodes an NB-ARC domain-containing protein, while Os03g0328000 encodes a DOCK family guanine nucleotide exchange factor involved in the defense response against fungus 39 .Similarly, the genes Os03g0674700/ OsGRF9 and Os04g0578000/ACS2 in MQTL3.4 and MQTL4.5, respectively, are known to enhance resistance against blast disease 40,41 .The characterized genes Os06g0286700/Piz and Os06g0286500/Nbs1-Pi9 in MQTL6.3 and Os09g0327600/Os09g0327800/Pi3 in MQTL9.2 encode RGA, which is known to confer broad-spectrum resistance against rice blast 10,42,43 .Furthermore, the genes Os08g0511700/pi55-1 and Os08g0512200/pi55-2 in MQTL8.5 encode LRR and heavy metal-associated domain-containing proteins, which are candidates for pi55(t) 31 .Furthermore, the Os10g0548300/OsPUB53 gene in MQTL10.3harbors U-box and modified ring finger domains, indicating its role in the plant defense pathway 44,45 .MQTL11.8 harbors Pi54/Pikh, a characterized gene for broad-spectrum resistance against blast 46 .These MQTLs span ≤ 2 Mb intervals and are amenable to marker-assisted selection (MAS) 47 .The peak/nearest markers of significant MQTL4.5, MQTL9.2 and MQTL11.8 were validated for their association with blast disease resistance.Since the ten genotypes utilized for validation were chosen from a diverse set of genotypes, the results can extrapolate to any new set of rice genotypes expecting the similar results.However, one should be careful while extrapolating the findings as that these markers can be assayed only for resistance or susceptibility regulated by corresponding MQTL regions and not for complete resistance to blast disease which in this case is a quantitative trait known to be regulated by several loci.The in-silico validation results may be extrapolated to other genotypes based on the presence of corresponding MQTL.Apart from that, the information of MQTLs identified and validated in the present study can be utilized in genomic prediction-based trait improvement programs by considering MQTL information as additive effects while predicting using genomic information.These markers could be used as surrogates in MAS for blast resistance breeding.Furthermore, in silico expression analysis of Os11g0639100/Pi54/Pikh, which resides within MQTL11.8, revealed differential expression of the Kyu77-07A strain incompatible and incompatible reactions to leaf infection, which validated the significance of the MQTL.Similarly, the genes within MQTL2.3 (Os02g0584700), MQTL6.2 (Os06g0163000/OsPUB70) and MQTL11.9(Os11g0682600/Pb2 and Os11g0689100/Pik) also exhibited similar expression patterns.Furthermore, the validation using peak markers suggests that the markers can be directly utilized to screen germplasm or new breeding lines for identification of resistant lines.Since the ten genotypes utilized for validation were chosen from diverse set of genotypes, the results can extrapolate to any set of genotypes expecting the similar results.However, one should be careful while extrapolating that these markers can be assayed only for resistance or susceptibility regulated by corresponding www.nature.com/scientificreports/MQTL regions and not for complete resistance to blast disease which a quantitative trait known to regulated by several loci.On the other hand, the results of the in-silico validation of genes in significant MQTL regions helps to understand the genes regulate resistance and susceptibility.The in-silico validation results may be extrapolated to other genotypes based on the presence of corresponding MQTL.The genes on chromosome 9 and 11 correspond to MQTL 9.2 and MQTL 11.8 were showed to differential expressions in in silico validation.These two MQTL were validated using peak markers for differentiating susceptible and resistant genotypes.Hence, results are validated to have significant scope to utilize in improvement of resistance in rice.Apart from that, the information of MQTLs identified and genes validated in the present study can be utilized in genomic predictionbased trait improvement programs by considering MQTL information as additive effects while predicting using genomic information 47 .The results presented in our study will help researchers decipher the complex network of genes and pathways underlying rice blast resistance.Furthermore, the identified MQTLs have potential utility in blast disease resistance breeding programs.

Literature survey and data collation
An exhaustive bibliographic survey related to QTL mapping for blast resistance in rice has been performed (https:// schol ar.google.com/, https:// www.resea rchga te.net/) using keywords like rice blast, QTL, mapping, rice blast resistance, blast resistant genomic regions, blast QTL information, blast resistance genes etc. to retrieve the literature.Information on the size, parentage, and type of mapping population, mapping function, LOD score, and PVE (%) was collected for a total of 737 QTLs compiled from 53 studies reported from 14 different countries across the globe (Table 1).The mapping studies included F 2 , doubled haploids (DH), recombinant inbred lines (RIL), and backcross (BC) mapping populations, with population sizes ranging from 31 to 1123.Input files, i.e., genetic map information and QTL information, were prepared as per the requirements of Biomercator V4.2 17,48 .
Those studies that lacked genetic map information were excluded from the study.The map information included the details of the type of mapping population and the genetic position of the markers, while the QTL information included the QTL ID, trait, trait ontology ID, experimental year and location, LOD score, PVE (%) value with the QTL position, and confidence interval, as described in the Biomercator V4.2 user guide.

Consensus map and QTL projection
The consensus map required for QTL projection was developed using Biomercator V4.2 by combining the genetic maps of the published original QTL studies and the reference map of rice available in the Gramene database (https:// archi ve.grame ne.org/ db/ marke rs/ marker_ view).Individual map files consisting of map and QTL information for each of the 737 original QTLs were integrated with the consensus map for their projection on the consensus map.Among them, 435 were projected on the consensus map for MQTL projection, while the other initial QTLs were not projected due to low LOD or PVE% and large CI 13 .For the projection of the SNP markers linked to the original QTL, the nearest SSR marker on the reference map corresponding to the position of the SNP was considered 15 .The QTL position and confidence interval of the original QTL were used where available; otherwise, the CI was calculated using the Darvasi and Soller equation 49 , CI = 530 N * R 2 for the F 2 and BC populations and their modifications, CI = 287 N * R 2 and CI = 163 N * R 2 for the DH and RIL populations, respectively, in which N is the size of the population and R 2 is the phenotypic variation explained by the QTL 50 .

Meta-QTL analysis
Meta-QTL analysis was implemented on the consensus map of each chromosome with projected QTLs considering default parameters in Biomercator V4.2.As the initial QTL number was more than nine on each chromosome, the two-step 'Veyrieras' method of meta-analysis was used 16 .The Akaike information criterion (AIC), corrected AIC (AICc), AIC3, Bayesian information criterion (BIC) and average weight of evidence (AWE) were used to choose the best-fit model.The model with the lowest AIC value is the best-fit model, as it indicates the least loss of information 27 .A best-fit model was chosen, and the other parameters were retained as defaults to obtain meta-QTLs.Based on the information from the best-fit model, the significant MQTLs are listed along with the complete information.The MQTL weights generated by the software were used to sort the identified MQTLs into significant MQTL.

Validation of MQTLs
The peak or nearest marker to the position of the significant MQTL was considered for validation (Table S1).The sequences of these primers were retrieved from the Gramene Marker database (https:// archi ve.grame ne.org/ marke rs/) and synthesized for use in marker assays.Ten genotypes with differential responses to blast disease were selected from one of our experiments at the ICAR-NRRI and evaluated over two years (unpublished; Table S6).Disease scoring was performed following a standard protocol, and entries with a mean disease score ≤ 5.5 were considered to indicate quantitative resistance 51 .For the validation of peak markers of the significant MQTLs, we considered five entries with a mean disease score ≤ 4.5 as resistant and five entries with a mean disease score ≥ 7.5 as susceptible.DNA was extracted from leaf samples of ten genotypes using the CTAB method 52 .Genomic DNA was subjected to thermal amplification using corresponding primers (peak markers), and the amplified product was visualized through gel electrophoresis and a documentation system.The markers that differentiated the resistant and susceptible entries validated the corresponding MQTLs for blast resistance.

Mining candidate genes within the MQTL region
The physical position of the flanking markers for each MQTL on the consensus map was identified using a reference map.Using the 'Biomart' tool of Ensembl Plants 53 and the Gramene database (https:// archi ve.grame ne.org/ plant_ ontol ogy/ index.html), reported genes within the MQTL region were downloaded using the IRGSP 1.0 O. sativa japonica genome as a reference.The following gene sequences were used to identify RGA genes: NBS-LRR, LRR containing protein or kinase, MAPK, pathogenesis-related, NB-ARC domain containing genes, and elicitor protein.The gene description, gene ontology, and trait ontology related to blast disease resistance were identified using the RAP-DB (https:// rapdb.dna.affrc.go.jp/ search/) and Oryzabase databases (https:// shigen.nig.ac.jp/ rice/ oryza base/ downl oad/ gene).Functional annotation (gene ontology (GO) and relationships between query genes based on GO terms, gene functions, etc.) was performed using the shinyGO v0.741 (http:// bioin forma tics.sdsta te.edu/ go74/) online tool 54 .The Rice Expression Profile Database (RiceXPRO), a repository of 40 K microarray-based expression profiles, was utilized in our study because it is based on gene models in the RAP-DB.Furthermore, the RXP_3001 dataset was chosen for analysis.This dataset included the expression profiles of the genes in the whole leaf of Oryza sativa cv.Nipponbare inoculated with M. oryzae strains P91-15B and Kyu-077A in relation to water (H 2 O) treatment.In our study, the expression profiles of 199 candidate genes identified within 29 MQTLs were extracted from the RXP_3001 dataset in the RiceXPro Version 3 online tool/ database (https:// ricex pro.dna.affrc.go.jp/).

Phenotyping of rice lines for blast disease response
Screening for rice blast disease response was carried out in a standard uniform blast nursery (UBN) described previously 55 .Briefly, each test entry (20-30 plants/test entry) was raised in 1 m long rows on raised nursery beds with a 10 cm row spacing.One row of HR-12 (susceptible check) was sown after every 5 test entries and also along the boundaries to ensure adequate inoculum build-up and disease spread.Entries were screened in a UBN using augmented design during Kharif 2022 and 2023.In our experiment, we used a highly virulent strain MoK19-18 (NCBI GenBank Accession No. MT757287) isolated from a popular mega rice cultivar BPT-5204 following the spore drop technique 56 .The mycelium and conidia were brushed into distilled water and filtered through muslin cloth.The spore concentration was adjusted approximately to 1 × 10 5 /mL −1 .The spore suspension containing Tween-20 (0.2%) was sprayed uniformly over all the entries after 25 days post-sowing (2-3 leaf stage) during the late evening hours, and plants were covered with a plastic tarpaulin overnight to create humid condition (> 90%).Further, water was sprayed 4-6 times a day for seven days to maintain high humidity and facilitate the disease development.Parallelly, blast disease pressure in the screening plots was augmented as described previously 57 .Briefly, blast disease-infected leaf samples were collected from naturally infected diseased plants under field conditions and were incubated overnight in a polythene cover to promote sporulation.Later, M. oryzae spores were harvested, and the spore suspension was sprayed uniformly on all entries.This process was repeated continuously for 3-4 days in an interval of 3 days.This approach specifically helpful for slow blast phenotyping, where the disease occurrence is slow 57 .Disease scoring was done after the susceptible check reached the highest score ( 9), approximately 7 days post inoculation, and scoring was repeated six times in 5-day intervals to avoid the escape of slow-blasting genotypes.A 0-9 scale devised by IRRI, Philippines 58 , was used to record the disease reaction.

Orthology of genes in O. sativa indica, Z. mays and A. thaliana
The O. sativa indica, maize, and Arabidopsis orthologs of genes within the MQTL region were identified using the 'Biomart' tool of the Ensembl Plants database 53 .The genes within MQTLs that were orthologous to the genes in the O. sativa indica, maize, and Arabidopsis genomes were visualized in a Circos plot using R software.The orthology of genes related to rice blast resistance was obtained through the g:Profiler online tool 59 .

Figure 1 .
Figure 1.Frequency distribution of (a) population size of each of the mapping populations used, (b) LOD score of the original QTLs and (c) phenotypic variation (%) explained by the original QTL.Graphical representation of the proportions of independent studies (d) using different biparental mapping populations and (d) countries from where the study is reported.

Figure 2 .
Figure 2. Depiction of the decrease in the number of original QTLs through MQTL analysis as represented in the chromosome-wise distribution (a) and (b) the number of original QTLs within MQTLs.

Figure 3 .
Figure 3. Representation of MQTLs harboring genetic loci associated with blast resistance.Loci represented in red indicates characterized blast resistance genes, green indicates RGAs, blue indicates genes associated with blast resistance as per the gene description, and black indicates loci with trait ontology associated with blast resistance.

Figure 4 .
Figure 4. Different (a) Gene Ontology terms and (b) trait classes associated with genes within M-QTL regions.

Figure 5 .
Figure 5. Functional annotation of genes related to rice blast resistance within the top 15 MQTLs.(a) Representation of the number of genes involved in different functional annotations (domain/motif/pathway) with fold enrichment.(b) network of genes involved in the expression of a function (domain/motif/pathway).

Table 1 .
Details of the QTL mapping studies on rice blast resistance used for meta-QTL analysis.

Table 2 .
List of the 15 significant MQTLs associated with rice blast resistance.Significant values are in bold.
MQTLChr Position Peak/Nearest marker Weight No.

Table 3 .
Details of meta-QTL regions associated with characterized blast resistance genes.Significant values are in bold.

Table 4 .
Functional annotation associated with rice blast resistance genes within the top 15 MQTLs.*number of query genes involved in the function.† The total number of genes in the gene network involved in functional annotation.